The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index.
The perceptron: A probabilistic model for information storage and organization in the brain.
Abstract
If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Representation of Events in Nerve Nets and Finite Automata
Kleene, S. (1951) · Princeton University Press eBooks · doi:10.1515/9781400882618-002 · Cited by 1,927
Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components
von Neumann, J. (1956) · Cited by 1,652
(1952) · Medical Entomology and Zoology · Cited by 1,436
Kneale, M., & von Hayek, F. A. (1954) · The Philosophical Quarterly · doi:10.2307/2216396 · Cited by 494
Human Nature in the Light of Psychopathology
Goldstein, K. (1940) · Harvard University Press eBooks · doi:10.4159/harvard.9780674492103 · Cited by 286
Milner, P. M. (1957) · Psychological Review · doi:10.1037/h0042287 · Cited by 235
Human Nature in the Light of Psychopathology.
L., H. A., & Goldstein, K. (1941) · The Journal of Philosophy · doi:10.2307/2017294 · Cited by 126
McCulloch, W. S. (1950) · dialectica · doi:10.1111/j.1746-8361.1950.tb01019.x · Cited by 73
Cited by
Sorted by citations
Items 3,901–4,000 of 13,333
Fierro, E. N., Faúndez, C. A., & Muñoz, A. S. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.116417 · Cited by 14
Transfer learning for radioactive particle tracking
Lindner, G., Shi, S., Vučetić, S., & Mišković, S. (2021) · Chemical Engineering Science · doi:10.1016/j.ces.2021.117190 · Cited by 14
A Review of Applications of Artificial Intelligence in Gastroenterology
Nawab, K., Athwani, R., Naeem, A., Hamayun, M., & Wazir, M. (2021) · Cureus · doi:10.7759/cureus.19235 · Cited by 14
Yu, X., & Shu, C. (2021) · La Matematica · doi:10.1007/s44007-021-00004-9 · Cited by 14
Critical window of the symmetric perceptron
Altschuler, D. J. (2023) · Electronic Journal of Probability · doi:10.1214/23-ejp1024 · Cited by 14
An Online Framework for Learning Novel Concepts over Multiple Cues
Luo, J., Orabona, F., & Barbara Caputo, B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12307-8_25 · Cited by 14
Regret Bounds for Hierarchical Classification with Linear-Threshold Functions
Cesa‐Bianchi, N., Conconi, A., & Gentile, C. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27819-1_7 · Cited by 14
Jakob von Uexküll and the origins of cybernetics
(2001) · Semiotica · doi:10.1515/semi.2001.047 · Cited by 14
Implicit and explicit representations
Rougier, N. P. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.01.008 · Cited by 14
Music genre classification using EMD and pitch based feature
Sarkar, R., & Saha, S. K. (2015) · International Conference on Advances in Pattern Recognition · doi:10.1109/icapr.2015.7050714 · Cited by 14
Nair, A., Hanvanich, S., & Cavusgil, S. T. (2007) · International Business Review · doi:10.1016/j.ibusrev.2007.08.005 · Cited by 14
Reliability modeling of flexible manufacturing systems
Yuanidis, P., Styblinski, M. A., Smith, D. R., & Singh, C. (1994) · Microelectronics Reliability · doi:10.1016/0026-2714(94)90505-3 · Cited by 14
Neuroinspired Architecture for Robust Classifier Fusion of Multisensor Imagery
Bogdanov, A. V. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.916214 · Cited by 14
Discriminative Binaural Sound Localization
Ben-Reuven, E., & Singer, Y. (2002) · Cited by 14
Correntropy in Data Classification
Syed, M. N., Príncipe, J. C., & Pardalos, P. M. (2012) · Springer proceedings in mathematics & statistics · doi:10.1007/978-1-4614-3906-6_5 · Cited by 14
Deep neural networks – a developmental perspective
Juang, B. H. (2016) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2016.9 · Cited by 14
Classification using single neuron
Yadav, R. N., Singh, V., & Kalra, P. K. (2004) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin.2003.1300258 · Cited by 14
Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning
Ye, J., Kobayashi, T., Iwata, M., Tsuda, H., & Murakawa, M. (2018) · Sensors · doi:10.3390/s18030833 · Cited by 14
From cybernetics to brain theory, and more: A memoir
Arbib, M. A. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.04.001 · Cited by 14
Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM
Adam, K., Smagulova, K., Krestinskaya, O., & James, A. P. (2018) · IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS) · doi:10.1109/edaps.2018.8680907 · Cited by 14
Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection
Liu, Y., Yan, Y., Chen, L., Han, Y., & Yang, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014408 · Cited by 14
Design of a Classification Model for a Twitter-Based Streaming Threat Monitor
Alves, F., Ferreira, P., & Bessani, A. (2019) · International Conference on Dependable Systems and Networks workshops · doi:10.1109/dsn-w.2019.00010 · Cited by 14
On the suitability, requisites, and challenges of machine learning [Invited]
Morais, R. M. (2020) · Journal of Optical Communications and Networking · doi:10.1364/jocn.401568 · Cited by 14
Damper, R. I., Gunn, S. R., & Gore, M. O. (2000) · Applied Intelligence · doi:10.1023/a:1008359903796 · Cited by 14
Neural nets: From Hartley and Hebb to Hinton
Valentine, E. R. (1989) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(89)90014-x · Cited by 14
Feature-Driven Emergence of Model Graphs for Object Recognition and Categorization
Westphal, G., von der Malsburg, C., & Würtz, R. P. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-76831-9_7 · Cited by 14
Durkin, J. (2002) · Elsevier eBooks · doi:10.1016/b978-012443880-4/50045-4 · Cited by 14
Learning and inference in weighted logic with application to natural language processing
McCallum, A., & Culotta, A. (2008) · Cited by 14
Manufacturing feature identification
Henderson, M. (1994) · Springer eBooks · doi:10.1007/978-94-011-0713-6_10 · Cited by 14
Efficient Learning with Virtual Threshold Gates
Maass, W., & Warmuth, M. K. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50054-2 · Cited by 14
Learning with online constraints: shifting concepts and active learning
Jaakkola, T., & Monteleoni, C. (2006) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 14
Next Generation Artificial Neural Networks for Civil Engineering
Flood, I. (2006) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(2006)20:5(305 · Cited by 14
Towards End-to-End Speech Recognition
Palaz, D. (2016) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-7054 · Cited by 14
Milin, P., Madabushi, H. T., Croucher, M., & Divjak, D. (2020) · arXiv (Cornell University) · doi:10.25500/edata.bham.00000449 · Cited by 14
Chen, S., Koehler, F., Moitra, A., & Yau, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04787 · Cited by 14
Why is AI hard and Physics simple?Preprint
Roberts, D. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00008 · Cited by 14
Convolutional Neural Networks for BeginnersPreprint
Tareen, S. A. K., & Tareen, F. K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4566310 · Cited by 14
Geometry Linked to Untangling Efficiency Reveals Structure and Computation in Neural PopulationsPreprint
Chou, C.-N., Kim, R., Arend, L. A., Yang, Y.-Y., Mensh, B. D., Shim, W. M., Perich, M. G., & Chung, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.26.582157 · Cited by 14
RosenPy: An open source Python framework for complex-valued neural networks
Cruz, A. A., Mayer, K. S., & Arantes, D. S. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101925 · Cited by 14
Holistic processing of hierarchical structures in connectionist networks
Neumann, J. (2001) · ERA · Cited by 14
Modeling of Multilayer Perceptron Neural Network Hyperparameter Optimization and TrainingPreprint
Elansari, T., Ouanan, M., & Bourray, H. (2023) · Research Square · doi:10.21203/rs.3.rs-2570112/v1 · Cited by 14
Advancements in High-Resolution Computed Tomography: Revolutionising Bone Health Micro-Research
Lindtner, R. A., Kampik, L., Putzer, D., Klosterhuber, M., Pallua, A., Streif, W., Schirmer, M., Degenhart, G., Arora, R. R., & Pallua, J. D. (2025) · Bioengineering · doi:10.3390/bioengineering12111189 · Cited by 13
Carbajal, C., Barja, A., & Pizarro, S. (2025) · Frontiers in Soil Science · doi:10.3389/fsoil.2025.1673628 · Cited by 13
Alwanin, R., Ismail, M. M. B., & Bchir, O. (2025) · Scientific Reports · doi:10.1038/s41598-025-33382-z · Cited by 13
Asadi, Y. (2026) · International Journal of Pavement Research and Technology · doi:10.1007/s42947-026-00711-y · Cited by 13
Zheng, B., Yang, J., Li, J., Chen, P., Xu, Y., Liu, D., Han, Y., & Chu, X. (2026) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2026.118741 · Cited by 13
Tao, D., Li, J., Du, K., & Lu, Z. (2026) · Bulletin of Earthquake Engineering · doi:10.1007/s10518-026-02408-w · Cited by 13
Moustris, K. P., Kavvadias, K. A., Kokkosis, A., & Paliatsos, A. G. (2016) · Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower) · doi:10.1049/cp.2016.1093 · Cited by 13
Machine Learning Approaches to Predict Default of Credit Card Clients
Liu, R. (2018) · Modern Economy · doi:10.4236/me.2018.911115 · Cited by 13
The Opportunities and Limitations of Using Artificial Neural Networks in Social Science Research
Pukelis, L., & Stančiauskas, V. (2019) · Politologija · doi:10.15388/polit.2019.94.2 · Cited by 14
Current Status and Future Direction of Artificial Intelligence in Healthcare and Medical Education
Jung, J. S. (2020) · Korean Medical Education Review · doi:10.17496/kmer.2020.22.2.99 · Cited by 13
Imperceptible Image Steganography Using Symmetry-Adapted Deep Learning Techniques
Khalifa, A. S., & Guzman, A. (2022) · Symmetry · doi:10.3390/sym14071325 · Cited by 13
Nadizar, G., Medvet, E., Nichele, S., & Pontes-Filho, S. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110610 · Cited by 13
Machine Learning in Computer Aided Engineering
Montáns, F. J., Cueto, E., & Bathe, K. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-36644-4_1 · Cited by 13
Gallet, M., Atto, A. M., Karbou, F., & Trouvé, E. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3342990 · Cited by 13
Fernández, S. P., Roca, A. B., González-Gutiérrez, C., Rodríguez, J. R., Álvarez, S. I., Fernández, J., & de Cos, F. J. (2024) · Photonics · doi:10.3390/photonics11030240 · Cited by 13
Taheri, S., González, M. A., Ruiz‐López, M. J., Magallanes, S., Delacour‐Estrella, S., Lucientes, J., Bueno‐Marí, R., la Puente, J. M., Bravo‐Barriga, D., Carrión, E. M. F., et al. (2024) · Emerging Microbes & Infections · doi:10.1080/22221751.2024.2343911 · Cited by 13
A framework for computer-aided high performance titanium alloy design based on machine learning
An, S., Li, K., Zhu, L., Liang, H., Ma, R., Liao, R., & Murr, L. E. (2024) · Frontiers in Materials · doi:10.3389/fmats.2024.1364572 · Cited by 13
Synergies between machine learning and reasoning - An introduction by the Kay R. Amel group
Baaj, I., Bouraoui, Z., Cornuéjols, A., Denœux, T., Destercke, S., Didier Dubois, A., Lesot, M., Marques-Silva, J. P., Mengin, J., Prade, H. M., et al. (2024) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2024.109206 · Cited by 13
Fei, F., Xiao, Y., Wang, L., Wang, Y., Fukuda, H., Yao, W., Yu, H., & Dong, Q. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105597 · Cited by 13
Zhan, Y., Liu, L., & Li, K. (2024) · IET Optoelectronics · doi:10.1049/ote2.12120 · Cited by 13
Chang, J., & Hatfield, B. S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.05.006 · Cited by 13
Romshoo, B., Patil, J., Michels, T., Muller, T. G., Kloft, M., & Pöhlker, M. L. (2024) · Atmospheric chemistry and physics · doi:10.5194/acp-24-8821-2024 · Cited by 13
Quantum convolutional neural networks for jet images classification
Elhag, H., Hartung, T., Jansen, K., Nagano, L., Pirina, G., & Tucci, A. D. (2026) · Quantum Machine Intelligence · doi:10.1007/s42484-026-00457-7 · Cited by 13
Landsat data reveal lake deoxygenation worldwide
Tu, Z., Zhang, Y., Shi, K., Gong, S., & Zhao, Z. (2024) · Water Research · doi:10.1016/j.watres.2024.122525 · Cited by 13
Explainable biology for improved therapies in precision medicine: AI is not enough
Jurišica, I. (2024) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2024.102006 · Cited by 13
Intelligent Prediction of Rate of Penetration Using Mechanism-Data Fusion and Transfer Learning
Huang, Z., Zhu, L. H., Wang, C., Zhang, C., Li, Q., Jia, Y., & Wang, L. (2024) · Processes · doi:10.3390/pr12102133 · Cited by 13
Danchin, A. (2024) · Microbial Biotechnology · doi:10.1111/1751-7915.70014 · Cited by 13
The Basics of Artificial Intelligence with Applications in Healthcare and Neurosurgery
Abrahams, J. M. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.10.105 · Cited by 13
Hangaragi, S., Neelima, N., Beemkumar, N., Kulshreshta, A., Khan, U. A., Akbar, N. S., Kanan, M. A., & Mahmoud, M. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.11.010 · Cited by 13
Mask, G. M., Wu, X., & Nicholson, C. (2024) · Gas Science and Engineering · doi:10.1016/j.jgsce.2024.205522 · Cited by 13
A framework for developing a machine learning-based finite element model for structural analysis
Li, G., Luo, R., & Yu, D. (2024) · Computers & Structures · doi:10.1016/j.compstruc.2024.107617 · Cited by 13
Secco, J., Spinazzola, E., Pittarello, M., Ricci, E., & Pareschi, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-81521-9 · Cited by 13
Time-series forecasting of microbial fuel cell energy generation using deep learning
Hess-Dunlop, A., Kakani, H., Taylor, S., Louie, D., Eshraghian, J. K., & Josephson, C. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1447745 · Cited by 13
Pushpa, G. G., Chaitra, M. S., Kolur, L. P., Dhananjaya, S., Kavyasri, M. N., Sunitha, R., & Kumar, A. P. (2025) · Ingénierie des systèmes d information · doi:10.18280/isi.300118 · Cited by 13
Wang, L., Simopoulos, C. M. A., Serrana, J. M., Ning, Z., Li, Y., Sun, B., Yuan, J., Figeys, D., & Li, L. (2025) · Microbiome · doi:10.1186/s40168-024-02015-4 · Cited by 13
Zhou, P., Liang, J., Liu, Y., Wu, J., Song, Q., & Li, X. (2025) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2025.102444 · Cited by 13
Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks
Annesi, B. L., Malatesta, E. M., & Zamponi, F. (2025) · SciPost Physics · doi:10.21468/scipostphys.18.4.118 · Cited by 13
A Historical Perspective on Artificial Intelligence in Applied Life Sciences
Emara, T. Z., Hassan, E., Elbedwehy, S., & Saber, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9208-9.ch002 · Cited by 13
Neural combinatorial optimization: A tutorial
Angioni, D., Archetti, C., & Speranza, M. G. (2025) · Computers & Operations Research · doi:10.1016/j.cor.2025.107102 · Cited by 13
On herbarium specimen images and artificial intelligence
Tessler, M., & Little, D. P. (2025) · New Phytologist · doi:10.1111/nph.70312 · Cited by 13
Jia, P., Sun, M., Lin, Y., & Yan, J. (2025) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2025.143686 · Cited by 13
Learning about environmental geometry: A flaw in Miller and Shettleworth's (2007) operant model.
Dawson, M. R. W., Kelly, D. M., Spetch, M. L., & Dupuis, B. (2008) · Journal of Experimental Psychology Animal Behavior Processes · doi:10.1037/0097-7403.34.3.415 · Cited by 13
Zhu, Y., Gao, W., & Zhou, Z. (2015) · Cited by 13
Vayre, J.-S. (2014) · Les cahiers du numérique · doi:10.3166/lcn.10.1.95-125 · Cited by 13
Perrot, B., Hardouin, J.-B., Costes, J., Caillon, J., Grall‐Bronnec, M., & Challet‐Bouju, G. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-014600 · Cited by 13
Mohebian, R., Riahi, M. A., & Afjeh, M. (2018) · Carbonates and Evaporites · doi:10.1007/s13146-017-0411-0 · Cited by 13
Reconstruction of Hydraulic Data by Machine Learning
Lapeyre, C., Cazard, N., Roy, P. T., Ricci, S., & Zaoui, F. (2020) · Springer water · doi:10.1007/978-981-15-5436-0_54 · Cited by 13
Waszkiewicz, S., Krakowska, P., & Puskarczyk, E. (2019) · Acta Geophysica · doi:10.1007/s11600-019-00347-6 · Cited by 13
Meier, P., Rohrmann, K., Sandner, M., & Prochaska, M. (2020) · IEEE Access · doi:10.1109/access.2020.3012064 · Cited by 13
Artificial neural network models to predict lost circulation in natural and induced fractures
Alkinani, H. H., Al-Hameedi, A. T. T., & Dunn‐Norman, S. (2020) · SN Applied Sciences · doi:10.1007/s42452-020-03827-3 · Cited by 13
Embedding for Anomaly Detection on Health Insurance Claims
Lü, J., Fung, B. C. M., & Cheung, W. K. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00060 · Cited by 13
A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems
Guo, Y., Zou, X., Hu, Y., Yang, Y., Wang, X., He, Y., Kong, R., Guo, Y., Li, G., Zhang, W., et al. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100054 · Cited by 14
Quantum neuron with real weights
Monteiro, C. A., Filho, G. I. S., Costa, M. H. J., Neto, F. M. D. P., & de Oliveira, W. R. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.07.034 · Cited by 13
QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning
Bharadi, V. A. (2021) · Wiley eBooks · doi:10.1002/9781119792345.ch3 · Cited by 13
Finding quadruply imaged quasars with machine learning – I. Methods
Akhazhanov, A., More, A., Amini, A. A., Hazlett, C. J., Treu, T., Birrer, S., Shajib, A. J., Liao, K., Lemon, C., Agnello, A., et al. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac925 · Cited by 13
Learning general halfspaces with general Massart noise under the Gaussian distribution
Diakonikolas, I., Kane, D. M., Kontonis, V., Tzamos, C., & Zarifis, N. (2022) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3519935.3519970 · Cited by 13
Bataineh, A. S. A., & Jarrah, A. A. (2021) · International Journal of Applied Metaheuristic Computing · doi:10.4018/ijamc.292500 · Cited by 13
Zheng, D. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/7260806 · Cited by 13
Robustness Against Adversarial Attacks Using Dimensionality
Chattopadhyay, N., Chatterjee, S., & Chattopadhyay, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95085-9_12 · Cited by 13
Metadata
- Authors
- Rosenblatt, F.
- Corresponding author
- Rosenblatt, F.
- Venue
- Psychological Review
- Published
- 1958
- Volume
- 65
- Issue
- 6
- Pages
- 386-408
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W2040870580
- Semantic Scholar
- 5d11aad09f65431b5d3cb1d85328743c9e53ba96
- Cited by
- 12,280
- References
- 15
- Citations by year
- 2026: 659 · 2025: 1019 · 2024: 969 · 2023: 933 · 2022: 945 · 2021: 965 · 2020: 898 · 2019: 826 · 2018: 494 · 2017: 411 · 2016: 318 · 2015: 313 · 2014: 282 · 2013: 332 · 2012: 255
- FWCI
- 14.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- perceptron, probabilistic models, information storage, memory, perceptual identification, generalization, memory retrieval
- MeSH terms
- Brain, Humans, Perception, Models, Statistical, Information Storage and Retrieval, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review